Personalization driven by artificial intelligence transforms the way we interact with digital products. With increasingly sophisticated algorithms, companies can offer more intuitive, predictable and adapted experiences to the individual needs of users..
a report from McKinsey It points out that 71% of consumers expect personalized interactions and that brands that invest in it can increase their revenues by up to 40%. However, this scenario also raises questions about privacy, technological dependence and the limits of automation in the consumer experience.
Personalization has always been a differential in customer service, but until recently, it was a manual and laborious process. Today, AI does not just follow fixed rules. She learns from each interaction, dynamically adjusting recommendations to better understand users’ preferences.
But that doesn’t mean it’s easy. The big challenge is in training specific models for each company. This is where the automation paradox comes in: AI can replace certain functions, but it does not eliminate the need for the human factor – in fact, what happens is a reinvention of roles in the labor market. It is necessary to feed these models with relevant and contextualized data so that they really add value to the customer and, whoever understands this movement and adapts quickly, will have a huge competitive advantage.
Now, the great opportunity is not only in the optimization of processes, but in the creation of new business models. With AI, companies that previously did not have the scale to compete now manage to offer advanced customization and even new forms of monetization, such as services based on artificial intelligence on demand.
How can companies balance innovation and responsibility to ensure positive impacts?
AI has to be a facilitator, not a controller. Three key pillars list:
- Transparency and explanability: They are essential for users to understand how AI makes decisions. AI models cannot be “black boxes”; Clarity is needed on the criteria used, avoiding distrust and questionable decisions;
- Privacy and security from design: Security and data protection cannot be a “patch” after the product is ready. This has to be thought of from the beginning of development;
- Multidisciplinary teams and continuous learning: AI requires integration between technology, product, marketing and customer service. If the teams don’t work together, the implementation can become misaligned and ineffective.
Customization and usability of digital products
The impact of AI on personalization comes from the ability to process and learn from large volumes of data in real time. Previously, personalization depended on static rules and fixed segmentations. Now, with linear regression combined with neural networks, systems learn and adjust recommendations dynamically, following user behavior.
This solves a critical problem: scalability. With AI, companies are able to offer hyper-personalized experiences without needing a gigantic team making manual adjustments.
In addition, AI is improving the usability of digital products, making interactions more intuitive and fluid. Some practical applications include:
- virtual assistants who really understand the context of conversations and improve with time;
- Recommendation Platforms that automatically adjust content and offers based on user preferences;
- systems of anticipation of needs, where the AI predicts what the user may need even before he looks for it.
AI is not just improving existing digital products, it is creating a new pattern of experience. The challenge now is to find balance: how to use this technology to create more humane and efficient experiences at the same time?
The key to innovating is to put the user at the center of the strategy. Well-implemented AI should add value without the user feeling that they have lost control over their data. Companies that balance innovation and responsibility will have a competitive advantage in the long term.
